Research Article
Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE
@INPROCEEDINGS{10.4108/eai.24-5-2024.2350156, author={Yunyi Gao}, title={Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE}, proceedings={Proceedings of the 3rd International Conference on Mathematical Statistics and Economic Analysis, MSEA 2024, May 24--26, 2024, Jinan, China}, publisher={EAI}, proceedings_a={MSEA}, year={2024}, month={10}, keywords={economic situation; long-term deposits; capital stability; machine learning}, doi={10.4108/eai.24-5-2024.2350156} }- Yunyi Gao
Year: 2024
Prediction of Long-term Deposit Customers Using SVM Optimized with Borderline-SMOTE
MSEA
EAI
DOI: 10.4108/eai.24-5-2024.2350156
Abstract
As the economic situation and market environments continually evolve, the financial sector faces numerous challenges, one of which is effectively enhancing the capacity to attract long-term deposits. Long-term deposits are crucial for the stability of a bank's capital and are also a critical factor in enabling banks to offer loans at lower costs. This paper aims to identify existing customers who are highly likely to subscribe to long-term deposits by employing machine learning techniques, thereby helping banks to more accurately target their marketing strategies and improve the efficiency of resource utilization.
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